Hitherto, techniques have been widely used in which an image feature is extracted from image data to perform the detection or identification of an object included in an image (hereinafter, referred to as “object detection”). A technique that uses Local Binary Patterns (hereinafter, referred to as “LBPs”) is disclosed in, for example, NPL 1 as one of such object detection techniques.
LBPs are each a binary pattern created by calculating differences in pixel values between each pixel of interest and pixels located in the surrounding neighborhood of the pixel of interest and arranging the resulting binary numbers. Gray scale patterns in an image can be extracted using LBPs.
The technique disclosed in NPL 1 and NPL 2 (hereinafter, referred to as a “first related art”) calculates a local binary pattern with respect to all or some pixels included in a certain region of an image targeted for identification (hereinafter, referred to as a “target image”). The first related art then generates a histogram of values of the LBPs as an image feature. The first related art then generates a classifier in advance using histograms generated from images including a predetermined object and images not including the object (hereinafter, collectively referred to as “training images”) and stores the classifier. The first related art then evaluates the histogram of the target image using the classifier to determine whether the target image includes the predetermined object.
Histograms of local binary patterns can represent differences in texture and gray scale patterns more accurately than image features such as histograms of oriented gradients (HOGs). Furthermore, the calculation of histograms of LBPs requires less processing cost compared with image features such as HOGs. Thus, the object detection using LBPs, such as the first related art, is expected to be applied to various fields.
A region targeted for an arithmetic operation of a local binary pattern is generally a 3 pixels×3 pixels region centering around the pixel of interest. However, there is a case where it is desired to use co-occurrence of features in a wider range by setting a wider region targeted for an arithmetic operation depending on the type of image or the type of object targeted for detection, and generating a local binary pattern from more pixels.
In this respect, for example, PTL 1 discloses a technique (hereinafter, referred to as a “second related art”) which sets a wider region of 5 pixels×5 pixels or only an outer circumferential portion of the region as a target for an arithmetic operation. Such a related art makes it possible to set a wider region targeted for an arithmetic operation of a local binary pattern.